During the urban distribution process, unexpected events may frequently result in disruptions to the current distribution plan, which need to be handled in real-time vehicle routing. In this paper, a knowledge-based m...
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During the urban distribution process, unexpected events may frequently result in disruptions to the current distribution plan, which need to be handled in real-time vehicle routing. In this paper, a knowledge-based modeling approach, PAM (disruption-handling Policies, local search algorithms and object-oriented Modeling), is developed, which combines the scheduling knowledge of experienced schedulers with the optimization knowledge concerning models and algorithms in the field of Operations Research to obtain an effective solution in real time. Experienced schedulers can respond to different disruptions promptly with heuristic adjustment based on their experience, but their solutions may be inaccurate, inconsistent, or even infeasible. This method is limited when the problem becomes large-scale. The model-algorithm method can handle large-scale problems, but it has to predefine a specific disruption and a specific distribution state for constructing a model and algorithm, which is inflexible, time-consuming and consequently unable to promptly obtain solutions for responding to different disruptions in real time. PAM modeling approach combines the advantages and eliminates the disadvantages of the two methods aforementioned. Computational experiments show that solutions achieved by this modeling approach are practical and the speed of achieving the solutions is fast enough for responding to disruptions in real time. (C) 2012 Elsevier B.V. All rights reserved.
Background: Protein structure prediction is an important but unsolved problem in biological science. Predicted structures vary much with energy functions and structure-mapping spaces. In our simplified ab initio prote...
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Background: Protein structure prediction is an important but unsolved problem in biological science. Predicted structures vary much with energy functions and structure-mapping spaces. In our simplified ab initio protein structure prediction methods, we use hydrophobic-polar (HP) energy model for structure evaluation, and 3-dimensional face-centred-cubic lattice for structure mapping. For HP energy model, developing a compact hydrophobic-core (H-core) is essential for the progress of the search. The H-core helps find a stable structure with the lowest possible free energy. Results: In order to build H-cores, we present a new Spiral searchalgorithm based on tabu-guided localsearch. Our algorithm uses a novel H-core directed guidance heuristic that squeezes the structure around a dynamic hydrophobic-core centre. We applied random walks to break premature H-cores and thus to avoid early convergence. We also used a novel relay-restart technique to handle stagnation. Conclusions: We have tested our algorithms on a set of benchmark protein sequences. The experimental results show that our spiral searchalgorithm outperforms the state-of-the-art local search algorithms for simplified protein structure prediction. We also experimentally show the effectiveness of the relay-restart.
In this study we present a combinatorial optimization method based on particle swarm optimization and local search algorithm on the multi-robot search system. Under this method, in order to create a balance between ex...
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In this paper, we present a formulation for the ready-mixed concrete scheduling problems with time-dependent travel time. Traffic congestion is a major problem for the business of delivering RMC. It may lead to late d...
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In this paper, we present a formulation for the ready-mixed concrete scheduling problems with time-dependent travel time. Traffic congestion is a major problem for the business of delivering RMC. It may lead to late deliveries of RMC to construction sites, and because RMC is perishable, late deliveries also mean large additional costs for both batch plants and the construction companies. Additional costs caused by traffic congestion can be reduced by taking predictable traffic congestion into account in the process of scheduling. For this purpose we develop a model with consideration of traffic congestion of RMC which uses a heuristic approach based on local search algorithm to solve. A detailed case study based on industrial data is used to illustrate the potential of the proposed approach.
In this paper, making use a exponential integral filter, a new algorithm for unconstrained global optimization is proposed. Compared with Yang's absolute value type integral filter method (Yang et al., Appl Math C...
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In this paper, making use a exponential integral filter, a new algorithm for unconstrained global optimization is proposed. Compared with Yang's absolute value type integral filter method (Yang et al., Appl Math Comput 18:173-180, 2007), this algorithm is more effective and more sensitive. Numerical results for some typical examples show that in most cases, this algorithm works effectively and reliably.
We consider mathematical programming problems with the so-called piecewise convex objective functions. A solution method for this interesting and important class of nonconvex problems is presented. This method is base...
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We consider mathematical programming problems with the so-called piecewise convex objective functions. A solution method for this interesting and important class of nonconvex problems is presented. This method is based on Newton's law of universal gravitation, multicriteria optimization and Helly's theorem on convex bodies. Numerical experiments using well known classes of test problems on piecewise convex maximization, convex maximization as well as the maximum clique problem show the efficiency of the approach.
Disruption management in urban distribution is the process of achieving a new distribution plan in order to respond to a disruption in real time. Experienced schedulers can respond to disruptions quickly with common s...
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Disruption management in urban distribution is the process of achieving a new distribution plan in order to respond to a disruption in real time. Experienced schedulers can respond to disruptions quickly with common sense and past experiences, but they often achieve the new distribution plan by a fuzzy, sometimes inconsistent, and not well-understood way. The method is limited when the problem becomes large scale or more complicated. In this case, optimization techniques consisting of models and algorithms may complement it. However, as the distribution system's state changes constantly with the plan-executing process and disruptions are diversified, real-time modeling is very difficult. Hence in order to achieve the real-time modeling process, the research in the paper focuses on a knowledge-based modeling method, which combines the knowledge of experienced schedulers with the OR knowledge concerning models and algorithms. Policies, algorithms and models are represented by proper knowledge representation schemes in order to support automated or semi-automated modeling by computers. The modeling process is demonstrated by a case to show how the different kinds of knowledge representation schemes cooperate with each other to support the modeling process. In the knowledge-based modeling process, based on the knowledge of experienced schedulers, a qualitative policy for handling the disruption based on the current distribution system's state is achieved firstly;and then based on OR knowledge, the corresponding model and algorithm are constructed to quantitatively optimize the policy. The integration of the two kinds of knowledge not only effectively supports the real-time modeling process, but also combines the advantages of both to achieve more practical and scientific solutions to different kinds of disruptions occurring under different distribution system's states. (C) 2011 Elsevier Ltd. All rights reserved.
A haplotype is a single nucleotide polymorphism (SNP) sequence and a representative genetic marker describing the diversity of biological organs. Haplotypes have a wide range of applications such as pharmacology and m...
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A haplotype is a single nucleotide polymorphism (SNP) sequence and a representative genetic marker describing the diversity of biological organs. Haplotypes have a wide range of applications such as pharmacology and medical applications. In particular, as a highly social species, haplotypes of the Apis mellifera (honeybee) benefit human health and medicine in diverse areas, including venom toxicology, infectious disease, and allergic disease. For this reason, assembling a pair of haplotypes from individual SNP fragments drives research and generates various computational models for this problem. The minimum error correction (MEC) model is an important computational model for an individual haplotype assembly problem. However, the MEC model has been proved to be NP-hard;therefore, no efficient algorithm is available to address this problem. In this study, we propose an improved version of a branch and bound algorithm that can assemble a pair of haplotypes with an optimal solution from SNP fragments of a honeybee specimen in practical time bound. First, we designed a local search algorithm to calculate the good initial upper bound of feasible solutions for enhancing the efficiency of the branch and bound algorithm. Furthermore, to accelerate the speed of the algorithm, we made use of the recursive property of the bounding function together with a lookup table. After conducting extensive experiments over honeybee SNP data released by the Human Genome Sequencing Center, we showed that our method is highly accurate and efficient for assembling haplotypes. (C) Korean Society of Applied Entomology, Taiwan Entomological Society and Malaysian Plant Protection Society, 2012. Published by Elsevier B.V. All rights reserved,
A phase-only adaptive processing based on direct data domain using hybrid genetic algorithms is presented. Considering practical application of the complex weight requires complex hardware system, the new algorithm ph...
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ISBN:
(纸本)9781467321013;9781467321006
A phase-only adaptive processing based on direct data domain using hybrid genetic algorithms is presented. Considering practical application of the complex weight requires complex hardware system, the new algorithm phase-only adaptive processing based on direct data domain is presented to reduce the complexity requirement of system implementation. Then a hybrid genetic algorithms combined genetic algorithm with local search algorithm is used to solve the phase-only adaptive weights. Experimental results show that the phase-only weights solved by a hybrid genetic algorithm not only form deep null in the direction of interferences, but also estimate the magnitude of the target signal accurately. The proposed algorithm achieves the purpose of interference suppression and detecting targets, as well as reduces the complexity of hardware implementation of the system greatly for engineer implement easily.
A phase-only adaptive processing based on direct data domain using hybrid genetic algorithms is *** practical application of the complex weight requires complex hardware system,the new algorithm phase-only adaptive pr...
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ISBN:
(纸本)9781467321006
A phase-only adaptive processing based on direct data domain using hybrid genetic algorithms is *** practical application of the complex weight requires complex hardware system,the new algorithm phase-only adaptive processing based on direct data domain is presented to reduce the complexity requirement of system *** a hybrid genetic algorithms combined genetic algorithm with local search algorithm is used to solve the phase-only adaptive *** results show that the phase-only weights solved by a hybrid genetic algorithm not only form deep null in the direction of interferences,but also estimate the magnitude of the target signal *** proposed algorithm achieves the purpose of interference suppression and detecting targets,as well as reduces the complexity of hardware implementation of the system greatly for engineer implement easily.
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